When spectrograms are fed to RNNs, these networks can detect and model changes in time by contrasting observations from current frames with information from previous ones. Their routines, then, would be analogous to those of the spectral flux onset detector, with RNNs taking information from more past frames into account. The original RNN model for onset detection can be found in [1]}. This time, the authors use three Bark spectrograms with constant hop size and different window sizes as inputs, and the resulting model is also optimised to handle less data than other onset detection algorithms. This makes it suitable for real-time onset detection, using unidirectional (causal) RNNs without LSTM cells. The implementation of the RNN-based onset detection method, which this study follows, is contained in Madmom's RNNOnsetProcessor [2]}. We use the non-real-time version, with bidirectional RNNs.
